2011/09/27 by Chenhao Tan, Lillian Lee, Tan, Chenhao +9 · 3 citations
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #H.2.8 #H.3.m #I.2.7 #Information Retrieval (cs.IR) #J.4 #Physics and Society (physics.soc-ph) #Sentiment Analysis and Opinion Mining #Statistics and Probability (physics.data-an) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1109.6018
openalex publication_date 2011/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower/followee network or from the network in Twitter formed by users referring to each other using "@" mentions. Our transductive learning results reveal that incorporating social-network information can indeed lead to statistically significant sentiment-classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.